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Fast and Efficient Food Quality Control Using Electronic Noses: Adulteration Detection Achieved by Unfolded Cluster Analysis Coupled with Time-Window Selection

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Abstract

The objective of this work is to report the improvements obtained in the discrimination of complex aroma samples with subtle differences in odor pattern, by the use of a fast procedure suitable for the cases of measurements in the field demanding decision-making in real time using a portable electronic nose. This device consists of a sensor array which records changes in conductivity as a function of time when aroma molecules reach the sensors. The core of the method consists of applying unfolded cluster analysis to selected time windows (UCATW) within the temporal evolution of the aroma profile recorded by the gas sensors, yielding an efficient, fast, and reliable data analysis tool that is easy to perform for electronic nose users. The performance of this data handling was tested in two case studies of food adulteration. The results demonstrated that this methodology enables to discriminate highly similar samples, herewith reducing the probability of achieving a wrong grouping due to the use of flawed data. The automation of this type of analysis is simple and improves the efficiency of the device significantly, herewith reducing the time of sensor’s signal recording that is necessary for a reliable assessment of the studied system. The results were validated by clustering the sample component scores that are obtained by applying parallel factor analysis (PARAFAC) to the original three-dimensional data array. An additional validation was obtained by means of a leave-one-out resampling procedure.

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Acknowledgments

D.L.B. is a member of the Scientific Career (CONICET), who would like to thank CONICET and MINCyT for the financial support: PIP 01210 and PICT BID 2006-00568. T.F.W. is a post-doctoral researcher at the Fund for Scientific Research (FWO), Flanders, Belgium. S.D.R. is a recipient of a post-doctoral fellowship from CONICET.

Conflict of Interest

Silvio D. Rodríguez declares that he has no conflict of interest. Diego A. Barletta declares that he has no conflict of interest. Tom F. Wilderjans declares that he has no conflict of interest. Delia L. Bernik declares that he has no conflict of interest. This article does not contain any studies with human or animal subjects.

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Authors and Affiliations

  1. Instituto de Química Física de Materiales, Ambiente y Energía (INQUIMAE), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Intendente Güiraldes 2160, Ciudad Universitaria, C1428EGA, Buenos Aires, Argentina

    Silvio D. Rodríguez, Diego A. Barletta & Delia L. Bernik

  2. Methodology of Educational Sciences Research Group, Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Andreas Vesaliusstraat 2, Box 3762, 3000, Leuven, Belgium

    Tom F. Wilderjans

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  1. Silvio D. Rodríguez
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  2. Diego A. Barletta
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  3. Tom F. Wilderjans
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  4. Delia L. Bernik
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Correspondence to Delia L. Bernik.

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Rodríguez, S.D., Barletta, D.A., Wilderjans, T.F. et al. Fast and Efficient Food Quality Control Using Electronic Noses: Adulteration Detection Achieved by Unfolded Cluster Analysis Coupled with Time-Window Selection. Food Anal. Methods 7, 2042–2050 (2014). https://doi.org/10.1007/s12161-014-9841-7

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  • DOI: https://doi.org/10.1007/s12161-014-9841-7

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